Prediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh

نویسندگان

1 Lecturer, Department of Computer, Torbat-e Heydariyeh branch, Islamic Azad University, Torbat-e Heydariyeh, Iran

2 Assistant Professor, Department of Water Engineering, Torbat-e Heydariyeh branch, Islamic Azad University, Torbat-e Heydariyeh, Iran

3 M.Sc. Graduated, Department of Computer, Torbat-e Heydariyeh branch, Islamic Azad University, Torbat-e Heydariyeh, Iran

چکیده

Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database includes 7336 records situated in 11 features from daily brainstorm data within a twenty year period. The samples were selected based on a case study in Torbat-e Heydariyeh. 70% were chosen for learning and 30% were chosen for taking tests. From 7181 available data, 75% and 25% were used for training and evaluating, respectively. This research studied the performance of different neural networks in order to predict precipitation and then presented an algorithm for combining neural networks with linear and nonlinear methods. After modeling and comparing their results using neural networks, the root mean square error was recorded for each method. In the first modeling, the artificial neural network error was 0.05, in the second modeling, linear combination of neural networks error was 0.07, and in the third model, nonlinear combination neural networks error was 0.001. Reducing the error of forecasting precipitation has always been one of the goals of the researchers. This study, with the forecast of precipitation by neural networks, suggested that the use of a more robust method called a nonlinear combination neural network can lead to improve men is in for cast diagnostic accuracy.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh

نویسندگان [English]

  • Iman Zabbah 1
  • Ali Reza Roshani 2
  • Amin Khafage 3
1 Lecturer, Department of Computer, Torbat-e Heydariyeh branch, Islamic Azad University, Torbat-e Heydariyeh, Iran
2 Assistant Professor, Department of Water Engineering, Torbat-e Heydariyeh branch, Islamic Azad University, Torbat-e Heydariyeh, Iran
3 M.Sc. Graduated, Department of Computer, Torbat-e Heydariyeh branch, Islamic Azad University, Torbat-e Heydariyeh, Iran
چکیده [English]

Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database includes 7336 records situated in 11 features from daily brainstorm data within a twenty year period. The samples were selected based on a case study in Torbat-e Heydariyeh. 70% were chosen for learning and 30% were chosen for taking tests. From 7181 available data, 75% and 25% were used for training and evaluating, respectively. This research studied the performance of different neural networks in order to predict precipitation and then presented an algorithm for combining neural networks with linear and nonlinear methods. After modeling and comparing their results using neural networks, the root mean square error was recorded for each method. In the first modeling, the artificial neural network error was 0.05, in the second modeling, linear combination of neural networks error was 0.07, and in the third model, nonlinear combination neural networks error was 0.001. Reducing the error of forecasting precipitation has always been one of the goals of the researchers. This study, with the forecast of precipitation by neural networks, suggested that the use of a more robust method called a nonlinear combination neural network can lead to improve men is in for cast diagnostic accuracy.

کلیدواژه‌ها [English]

  • Monthly rainfall
  • Artificial Neural Networks
  • experts’ mixture
  • Torbat-e Heydariyeh Precipitation

مراجع

Aksoy, H. and Dahamsheh, A., 2009, Artificial neural network models for forecasting monthly precipitation in Jordan. Stochastic Environmental Research and Risk Assessment, 23(7), 917–931.
Alizadeh, M. J. and Kavianpour, M. R., 2015, Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Mar Pollut Bull., 98, 171-178.
Bustami, R., Bessaih, N., Bong, C. and Suhaili, S., 2007, Artificial Neural Network for Precipitation and Water Level Predictions of Bedup River. IAENG International Journal of Computer Science, 34(2).
Chen, H., Guo, J., Xiong, W., Guo, S. L. and Xu, C.-Y., 2010, Downscaling GCMs  sing the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin. Adv. Atmos. Sci., 27(2), 274–284.
Fallah-Ghalhary, G. A., Habibi-Nokhandan, M., Mousavi-Baygi, M., Khoshhal, J. and Barzoki, A. S., 2010, Spring rainfall prediction based on remote linkage controlling using adaptive neuro-fuzzy inference system (ANFIS). Theoretical and Applied Climatology, 101(1–2), 217–233.
Fallah Ghalhery, Gh. and KHoshhal, J., 2011, Forecasting of spring precipitation of Khorasan based on large scale climate signals using neural network. Geography Studies. 69, 115-133.
Gazzaz, N. M., Yusoff, M. K., Aris, A. Z., Juahir, H. and Ramli, M. F., 2012, Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Marine pollution bulletin, 64(11), 2409-2420.
Halabiyan, A. and Darand, M., 2011, Estimation of Isfahan rainfall using artificial neural networks. Journal of Applied Geosciences Research. 12 (26): 47-63
Khalili, N., khodashenas, S. R. and Davari, K., 2007, Prediction of precipitation using artificial neural networks. In the Second Iranian Water Resources Conference.
Khalili, N., Khodashenas, S. R., Davary, K., Baygi, M. M. and Karimaldini, F., 2016, Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study. Arabian Journal of Geosciences, 9(13), 624.
Kisi, O., 2008, River flow forecasting and estimation using different artificial neural network techniques. Hydrology research, 39, 27-40
Moustris, K. P., Larissi, I. K., Nastos, P. T. and Paliatsos, A. G., 2011, Precipitation forecast using artificial neural networks in specific regions of Greece. Water Resources Management, 25(8), 1979–1993.
Nasseri, M., Asghari, K. and Abedini, M. J., 2008, Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Systems with Applications, 35(3), 1415-1421.
Nasseri, M. and Zahraie, B., 2011, Application of simple clustering on space time mapping of mean monthly rainfall pattern. International Journal of Climatology, 31(5), 732–741.
Nourani, V., Kisi, Ö. and Komasi, M., 2011, Two hybrid Artificial Intelligence approaches for modeling rainfall– runoff process. J. Hydrol., 402, 41–59.
Okkan, U., 2012, Wavelet neural network model for reservoir inflow prediction. Scientia Iranica, 19, 1445–1455. https://doi.org/10.1016/j.scient.
Rajaee, T., Nourani, V., Zounemat-Kermani, M. and Kisi, O., 2010, River suspended sediment load prediction: application ofANN and wavelet conjunction model. J. Hydrol. Eng., 16, 613–627.
Salajegheh, A., Fathabadi, A. and Najafi Hajiva, M., 2008, Comparison of Application Time Series and Artificial Neural Network Models in Drought Forecasting (Case Study: Khorasan Razavi Provinces), 2(4), 74–77.
Sattari, M., Rezazadeh Joody, A. and Nahrin, F., 2013, Estimation of Monthly Precipitation Using Artificial Neural Networks and Model M5 (Case Study: Ahar Station). Journal of Irrigation and Water Engineering, 46 (2), 247-260.
Sirhan, H. and Koch, M., 2013, Prediction of Dynamic Groundwater Levels in the Gaza Coastal Aquifer, South Palestine, using Artificial Neural Networks. Journal of Hydrocarbons Mines and Environmental Research, 4(1), 42-56.
Sharma, M., Mathew, L. and Chatterji, S., 2014, Weather forecasting using soft computing techniques. International Journal of Advanced Technology In Engineering And Science, 2(7), 473-480.
Tryland, I., Myrmel, M., Østensvik, Ø., Wennberg, A. C. and Robertson, L. J., 2014, Impact of rainfall on the hygienic quality of blue mussels and water in urban areas in the Inner Oslofjord, Norway. Marine pollution bulletin, 85(1), 42-49.
فیزیک زمین و فضا
دوره 44، شماره 4
دی 1397
صفحه 115-126

فایل ها

هم رسانی

ارجاع به این مقاله

آمار